Spherical U-Net For Infant Cortical Surface Parcellation - IEEE Xplore
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In this paper, we propose a novel end-to-end deep learning method by formulating surface parcellation as a semantic segmentation task on the sphere.
Jul 11, 2019 · In this paper, we propose a novel end-to-end deep learning method by formulating surface parcellation as a semantic segmentation task on the sphere.
Apr 1, 2019 · We propose the Spherical U-Net architecture by replacing all operations in the standard U-Net with their spherical operation counterparts.
This package includes the codes and network model trained based on UNC datasets for infant cortical surface parcellation using Spherical U-Net architecture.
Spherical U-Net for Infant Cortical Surface Parcellation - ISMRM
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Specifically, the U-Net and SegNet architectures are transformed to parcellate infant cortical surfaces. Experiments on 90 neonates indicate the superiority of ...
Specifically, we propose the Spherical U-Net architecture by replacing all operations in the standard U-Net with their spherical operation counterparts. We then ...
In this work, we propose a novel end-to-end deep learning method by formulating surface parcellation as a semantic segmentation task on the spherical space. To ...
May 29, 2024 · This study proposes the attention-gated spherical U-net, a novel deep-learning model designed for automatic cortical surface parcellation of the fetal brain.
Oct 22, 2024 · It was originally designed for cortical surface parcellation [10] and achieves state-of-the-art performance, which could be used as a generator ...
This paper is aiming to introduce a method that avoids both by working more closely on the data's natural topology.